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Equation 1 · Part 7 · Comparing the Main Approaches to AI Agent Architecture

≤

at∼πθ(a∣o≤t, ht, g),a_t \sim \pi_\theta(a \mid o_{\le t},\ h_t,\ g),
≤

What this part means

Less than or equal to.

Its job in the formula

Less than or equal to.

The passage around this formula

Strip away the branding and every one of these systems is choosing an action from a history of observations rather than from the true state of the world, because the true state is never directly available to it. That is the founding move of decision theory under partial observability: an agent’s policy must be a function of a belief built from what it has seen, not of the world as it actually is, and the two can silently diverge [ 11 ] . Write the general shape once, for a single decision-maker at a single step: at∼πθ(a∣o≤t, ht, g)a_t \sim \pi_\theta(a \mid o_{\le t},\ h_t,\ g). where o≤to_{\le t} is the observation history, hth_t is whatever state the system has chosen to retain, and g is the task goal. Nothing here is specific to language…

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Learn the underlying idea

An inequality compares values without claiming they are equal. It describes a range, threshold, or bound that a quantity may satisfy.

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Sources cited in the surrounding passage

These citations provide research context; check each source for the exact claim it supports.